class_docstr: Convert a collection of raw documents to a matrix of text-index
  features
  
  keep the order of word in document
  index start with 1, index 0 is default padding_value
  
  Parameters
  ----------
  
  embedding_size: size of embedding word to vector
code: def fit_transform(self, raw_documents):
      return self._transform(raw_documents, is_fit=True)
  
file_path: underthesea/underthesea/transformer/word_vector.py
imports: from functools import reduce
  from underthesea.transformer import Transformer
label_desc: Use image node_id 6 for calling the WordVectorTransformer obj's underlying member method code with example usage: obj.fit_transform(raw_documents) and returns: self